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References

Here are the references used during our research and documentation!

  • /@activatedgeek. “Policy Gradients in a Nutshell.” Medium, Towards Data Science, 2 June 2018, towardsdatascience.com/policy-gradients-in-a-nutshell-8b72f9743c5d.
  • /@aminamollaysa. “Policy Gradients and Log Derivative Trick.” Medium, Medium, 19 Nov. 2018, medium.com/@aminamollaysa/policy-gradients-and-log-derivative-trick-4aad962e43e0.
  • /@ardendertat. “Applied Deep Learning - Part 1: Artificial Neural Networks.” Medium, Towards Data Science, 9 Oct. 2017, towardsdatascience.com/applied-deep-learning-part-1-artificial-neural-networks-d7834f67a4f6.
  • /@ashish_fagna. “Understanding OpenAI Gym.” Medium, Medium, 23 Mar. 2018, medium.com/@ashish_fagna/understanding-openai-gym-25c79c06eccb.
  • /@bushaev. “Understanding RMSprop - Faster Neural Network Learning.” Medium, Towards Data Science, 2 Sept. 2018, towardsdatascience.com/understanding-rmsprop-faster-neural-network-learning-62e116fcf29a.
  • /@gencozgur. “Notes on Artificial Intelligence, Machine Learning and Deep Learning for Curious People.” Medium, Towards Data Science, 5 Feb. 2019, towardsdatascience.com/notes-on-artificial-intelligence-ai-machine-learning-ml-and-deep-learning-dl-for-56e51a2071c2.
  • /@hafidz. “Understanding Learning Rates and How It Improves Performance in Deep Learning.” Medium, Towards Data Science, 27 Jan. 2018, towardsdatascience.com/understanding-learning-rates-and-how-it-improves-performance-in-deep-learning-d0d4059c1c10.
  • /@lskhere. “Learning Rate Schedules and Adaptive Learning Rate Methods for Deep Learning.” Medium, Towards Data Science, 1 Aug. 2017, towardsdatascience.com/learning-rate-schedules-and-adaptive-learning-rate-methods-for-deep-learning-2c8f433990d1.
  • /@m.alzantot. “Deep Reinforcement Learning Demysitifed (Episode 2) - Policy Iteration, Value Iteration and Q-Learning.” Medium, Medium, 8 Oct. 2018, medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa.
  • /@m.alzantot. “Deep Reinforcement Learning Demysitifed (Episode 2) - Policy Iteration, Value Iteration and Q-Learning.” Medium, Medium, 8 Oct. 2018, medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa.
  • /@sagarsharma4244. “Policy Networks vs Value Networks in Reinforcement Learning.” Medium, Towards Data Science, 7 Aug. 2018, towardsdatascience.com/policy-networks-vs-value-networks-in-reinforcement-learning-da2776056ad2.
  • “5. Data Structures¶.” 5. Data Structures - Python 3.7.4 Documentation, docs.python.org/3/tutorial/datastructures.html.
  • “A Beginner's Guide to Backpropagation in Neural Networks.” Skymind, skymind.ai/wiki/backpropagation.
  • “A Beginner's Guide to Deep Reinforcement Learning.” Skymind, skymind.ai/wiki/deep-reinforcement-learning.
  • “A Beginner's Guide to Neural Networks and Deep Learning.” Skymind, skymind.ai/wiki/neural-network.
  • FAtBalloonFAtBalloon 1, et al. “What Does the Hidden Layer in a Neural Network Compute?” Cross Validated, 1 Dec. 1963, stats.stackexchange.com/questions/63152/what-does-the-hidden-layer-in-a-neural-network-compute.
  • ihadannyihadanny 57511 gold badge66 silver badges1616 bronze badges, et al. “Why Do We Normalize the Discounted Rewards When Doing Policy Gradient Reinforcement Learning?” Data Science Stack Exchange, 1 Dec. 1967, datascience.stackexchange.com/questions/20098/why-do-we-normalize-the-discounted-rewards-when-doing-policy-gradient-reinforcem.
  • Karpathy, Andrej. “Deep Reinforcement Learning: Pong from Pixels.” Deep Reinforcement Learning: Pong from Pixels, karpathy.github.io/2016/05/31/rl/.
  • McNulty, Eileen, et al. “What's The Difference Between Supervised and Unsupervised Learning?” Dataconomy, 10 June 2019, dataconomy.com/2015/01/whats-the-difference-between-supervised-and-unsupervised-learning/.
  • “Numpy.” PyPI, pypi.org/project/numpy/.
  • “Numpy.dot¶.” Numpy.dot - NumPy v1.17 Manual, docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html.
  • “Numpy.outer¶.” Numpy.outer - NumPy v1.17 Manual, docs.scipy.org/doc/numpy/reference/generated/numpy.outer.html.
  • “Numpy.vstack¶.” Numpy.vstack - NumPy v1.17 Manual, docs.scipy.org/doc/numpy/reference/generated/numpy.vstack.html.
  • OpenAI. “A Toolkit for Developing and Comparing Reinforcement Learning Algorithms.” Gym, gym.openai.com/docs/.
  • Oppermann. “Self Learning AI-Agents IV: Stochastic Policy Gradient.” Medium, Towards Data Science, 1 Aug. 2019, towardsdatascience.com/self-learning-ai-agents-iv-stochastic-policy-gradients-b53f088fce20.
  • “Pickle - Python Object Serialization¶.” Pickle - Python Object Serialization - Python 3.7.4 Documentation, docs.python.org/3/library/pickle.html.
  • “Reinforcement Learning.” Exploring Science, 7 June 2019, dashora7.wordpress.com/2019/06/07/reinforcement-learning/.
  • “Serialization Is Dead! Long Live Serialization!” Waratek, 6 Nov. 2018, www.waratek.com/serialization-is-dead-long-live-serialization/.
  • “Sigmoidal Nonlinearity.” DeepAI, 17 May 2019, deepai.org/machine-learning-glossary-and-terms/sigmoidal-nonlinearity.
  • “The Markov Property, Chain, Reward Process and Decision Process.” Xavier Geerinck - Blog, Xavier Geerinck - Blog, 20 May 2018, xaviergeerinck.com/markov-property-chain-reward-decision.
  • “Understanding Xavier Initialization In Deep Neural Networks.” PERPETUAL ENIGMA, 7 Apr. 2016, prateekvjoshi.com/2016/03/29/understanding-xavier-initialization-in-deep-neural-networks/.
  • user2991243user2991243 1, et al. “What Is Batch Size in Neural Network?” Cross Validated, 1 Nov. 1965, stats.stackexchange.com/questions/153531/what-is-batch-size-in-neural-network.
  • Woergoetter, Florentin, and Bernd Porr. “Reinforcement Learning.” Scholarpedia, www.scholarpedia.org/article/Reinforcement_learning#.28Temporal.29_Credit_Assignment_Problem.
  • “Wpovell's Blog.” Making a Pong AI with Policy Gradients, wpovell.net/posts/pg-pong.html.